feat(ppt): new_product 30-day tracking report v3.1.0

Wave 3.1：新品追蹤報告（PM/採購用） generate_new_product_ppt — 9 頁 - P1 封面：含新品力徽章（強勁/穩健/偏弱/疲弱，依業績佔比）+ 三亮點 - P2 KPI：新品總數/業績/佔比/平均單品業績 + AI 解讀 - P3 新品整體日業績曲線（爬榜軌跡 matplotlib） - P4 新品依品類分佈橫條 - P5-P7 新品 TOP 50（自動分頁） - P8 AI PM 戰術洞察 - P9 附錄 query_new_products(days_recent=30, days_baseline=60) - PostgreSQL CTE：recent EXCEPT early recent = 近 30 天有銷售 early = 31-90 天前有銷售 - 自動回傳：新品 TOP 50 / 子品類分佈 / 日業績曲線 - 含 ANY array 查詢新品集合的整體日業績 _ppt_ai_analysis 加 is_new_prod 分支 - 角色：PM 商品經理 + 採購主管 - 結構：新品力評估 / 明星新品識別 / 品類分佈與機會 / SMART 三層 / 風險預警 - SMART 行動含：加碼 TOP3 / 觀察排名 11-30 / 數據追蹤 - max_tokens 1800 業界基準：新品業績佔比 5-10% 為健康，>8% 強勁，<3% 偏弱路由： - /ppt new_product 預設近 30 天 - /ppt new_product 14 自訂追蹤天數 Telegram 按鈕：「🆕 新品 30 天追蹤」 bump TEMPLATE_VERSIONS['new_product'] = v3.1.0 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-03 12:37:06 +08:00
parent af6157f8ba
commit 95a74c3502
3 changed files with 436 additions and 0 deletions
--- a/routes/openclaw_bot_routes.py
+++ b/routes/openclaw_bot_routes.py
@@ -1889,6 +1889,7 @@ def _ppt_ai_analysis(prompt_data: str, report_type: str = '') -> str:
    is_customer   = '客戶' in report_type or 'customer' in report_type
    is_forecast   = '檔期前瞻' in report_type or 'forecast' in report_type
    is_promo_cmp  = '多活動' in report_type or 'promo_compare' in report_type
+    is_new_prod   = '新品' in report_type or 'new_product' in report_type

    # ── 格式鐵律（所有 prompt 共用後綴）────────────────────────
    FORMAT_RULES = (
@@ -2046,6 +2047,46 @@ def _ppt_ai_analysis(prompt_data: str, report_type: str = '') -> str:
            + FORMAT_RULES
        )
        max_tokens = 1400
+    elif is_new_prod:
+        sys_instruction = (
+            "你身兼 (1) PM 商品經理（精通新品上架 / 商品生命週期 / SKU 健康度）"
+            "(2) 採購主管（精通選品、新廠商引進、新品扶植）。\n"
+            "你的客戶是 momo PM 與採購團隊，會用本報告做新品扶植加碼、"
+            "曇花一現新品下架、明星新品行銷加碼的決策。\n\n"
+            f"請針對以下{report_type}資料，輸出新品戰術洞察，結構嚴格如下：\n\n"
+            "【新品力評估】（3-4 句）\n"
+            "引用新品數、新品業績、業績佔比，評估新品力等級（強勁 >8% / 穩健 3-8% / "
+            "偏弱 1-3% / 疲弱 <1%）；與業界平均（健康電商 5-10%）比較定位；"
+            "若 <3% 必須點明「新品引進不足，將失去成長動能」並建議下季加碼新品開發。\n\n"
+            "【明星新品識別】（3-4 句）\n"
+            "點名 TOP3 明星新品，分析高業績成因（檔期推力 / 品牌力 / 行銷投放 / "
+            "獨家代理）；建議哪 1-2 款適合做次月主推 hero SKU；"
+            "若 TOP1 新品業績 >NT$10 萬則建議升格為「常銷主力」加碼資源。\n\n"
+            "【品類分佈與機會】（3-4 句）\n"
+            "新品依品類分佈是否健康（過度集中於單一品類？）；"
+            "建議下季應加碼新品的品類（依 2026 趨勢：永續美妝 / 母嬰高端 / "
+            "機能性食品 / 男性保養 / 銀髮保健等）；"
+            "識別「新品荒漠」品類（無新品進駐者），建議優先填補。\n\n"
+            "【新品扶植與淘汰建議 — SMART 框架】\n"
+            "■ 立即執行（3 條，✅ 開頭）：\n"
+            "  ✅ 加碼：對 TOP3 新品（具體商品名）增加首頁版位/廣告預算 +X%，"
+            "預期業績 +Y%，期限：YYYY/MM/DD\n"
+            "  ✅ 觀察：對排名 11-30 名新品（具體商品名）2 週後回看，"
+            "若週業績 < NT$Z 則啟動下架評估\n"
+            "  ✅ 數據追蹤：建立新品 KPI 儀表板（爬榜速度 / 客單 / 復購率），"
+            "每週自動更新\n"
+            "■ 中期強化（2 條，✅ 開頭）：開發新廠商 / 跨品類聯名 / 自有品牌 OEM\n"
+            "■ 長期佈局（1 條，✅ 開頭）：建立新品引進 SOP（試銷 30 天 → "
+            "達標升常銷 / 不達標下架）\n\n"
+            "【最大風險與防禦】（2-3 句）\n"
+            "(a) 新品試銷失敗率高 → 建議單一品類下架率 >50% 觸發採購復盤\n"
+            "(b) 新品搶食常銷市場 → 觀察常銷商品銷量是否被新品稀釋\n"
+            "(c) 過度依賴單品爆款 → 建議新品 TOP1 佔新品業績 <30% 為健康\n\n"
+            "要求：每段引用至少 2 個具體數字，全文 800~1000 字，禁用模糊用詞。"
+            + MARKET_TREND_2026
+            + FORMAT_RULES
+        )
+        max_tokens = 1800
    elif is_promo_cmp:
        sys_instruction = (
            "你是資深行銷主管（10 年促銷活動策劃實戰經驗）。"
@@ -2750,6 +2791,7 @@ def _generate_ppt_cmd(sub_type: str, sub_arg: str, _chat_id: int, target: str,
            generate_vendor_ppt, generate_period_review_ppt,
            generate_category_deep_ppt, generate_customer_analytics_ppt,
            generate_forecast_pre_event_ppt, generate_promo_compare_ppt,
+            generate_new_product_ppt,
            check_pptx_available
        )
    except ImportError:
@@ -3274,6 +3316,58 @@ def _generate_ppt_cmd(sub_type: str, sub_arg: str, _chat_id: int, target: str,
        })
        return ppt_path

+    elif sub_type in ('new_product', 'newproduct', '新品', '新品追蹤'):
+        # /ppt new_product            預設 30 天追蹤
+        # /ppt new_product 14         自訂追蹤天數
+        days = 30
+        if sub_arg and sub_arg.isdigit():
+            days = int(sub_arg)
+        baseline_days = 60
+
+        params = {'report_type': 'new_product', 'days': days}
+        cached, cached_ai = _load_cached_ppt_path_and_analysis('new_product', params)
+        if cached:
+            return cached
+
+        mcp_text = ''
+        if not cached_ai:
+            mcp_text = _fetch_mcp_context()
+
+        np_data = query_new_products(days_recent=days, days_baseline=baseline_days)
+        if not np_data.get('found'):
+            raise RuntimeError(f'近 {days} 天無新品（前 {baseline_days} 天無交易但近期有銷售的商品）')
+
+        kpis = np_data.get('kpis', {})
+        top5_str = '\n'.join(
+            f"  {i+1}. {p.get('name','')[:30]} ({p.get('category','—')}) — "
+            f"NT${p.get('revenue', 0):,.0f}"
+            for i, p in enumerate(np_data.get('new_products', [])[:5])
+        )
+        sub_str = '\n'.join(
+            f"  - {c.get('name','')}: {c.get('sku_count', 0)} 款 / "
+            f"NT${c.get('revenue', 0):,.0f}"
+            for c in np_data.get('sub_categories', [])[:5]
+        )
+        data_summary = (
+            f"【追蹤期間】{np_data.get('period', '')}\n"
+            f"【新品總數】{kpis.get('new_count', 0)} 款\n"
+            f"【新品業績】NT${kpis.get('new_revenue', 0):,.0f}\n"
+            f"【業績佔比】{kpis.get('new_pct', 0):.1f}%（vs 整體 NT${kpis.get('total_revenue', 0):,.0f}）\n\n"
+            f"【新品 TOP 5】\n{top5_str}\n\n"
+            f"【新品依品類分佈】\n{sub_str}\n\n"
+            f"【MCP 外部市場情報】\n{mcp_text[:500] if mcp_text else '（無）'}"
+        )
+        ai_text = cached_ai or _ppt_ai_analysis(data_summary, '新品追蹤報告')
+        if not cached_ai and _ppt_needs_fallback(ai_text):
+            ai_text = _ppt_fallback_insight('新品追蹤', data_summary, mcp_text)
+
+        ppt_path = generate_new_product_ppt(np_data, ai_text)
+        _store_ppt_cache('new_product', params, ppt_path, {
+            'report_type': 'new_product', 'parameters': params,
+            'data_summary': data_summary, 'analysis': ai_text, 'mcp': mcp_text,
+        })
+        return ppt_path
+
    elif sub_type in ('promo_compare', 'promocompare', '促銷比較', '多活動'):
        # /ppt promo_compare 母親節:2026/05/05-2026/05/14|520:2026/05/18-2026/05/22|618:2026/06/14-2026/06/22
        # 用 | 分隔多場活動，每場用 : 分 label/dates
@@ -4721,6 +4815,123 @@ def query_date_range(start_str: str, end_str: str) -> dict:
        return {'found': False, 'range': f'{start_str}~{end_str}'}


+def query_new_products(days_recent: int = 30, days_baseline: int = 60) -> dict:
+    """新品追蹤：近 days_recent 天有銷售、過去 days_baseline 天無銷售的商品
+
+    回傳：{
+        period, kpis: {new_count, new_revenue, new_pct, top1_revenue},
+        new_products: [TOP 50 含日銷售軌跡],
+        sub_categories: [新品依品類分佈],
+        daily_total: [{date, new_revenue}],   # 新品整體日業績
+    }
+    """
+    try:
+        with _db().connect() as c:
+            # 主查詢：近 N 天 EXCEPT 早期
+            new_rows = c.execute(text(f"""
+                WITH recent AS (
+                    SELECT "商品ID", "商品名稱", "商品分類L1",
+                           SUM(CAST("總業績" AS FLOAT)) AS rev,
+                           SUM(CAST("數量" AS INTEGER)) AS qty,
+                           COUNT(DISTINCT "訂單編號") AS orders,
+                           MIN(CAST("日期" AS DATE)) AS first_seen
+                    FROM realtime_sales_monthly
+                    WHERE CAST("日期" AS DATE) >= CURRENT_DATE - INTERVAL '{days_recent} days'
+                    GROUP BY "商品ID", "商品名稱", "商品分類L1"
+                ),
+                early AS (
+                    SELECT DISTINCT "商品ID"
+                    FROM realtime_sales_monthly
+                    WHERE CAST("日期" AS DATE) BETWEEN
+                          CURRENT_DATE - INTERVAL '{days_recent + days_baseline} days' AND
+                          CURRENT_DATE - INTERVAL '{days_recent + 1} days'
+                )
+                SELECT recent.* FROM recent
+                LEFT JOIN early ON recent."商品ID" = early."商品ID"
+                WHERE early."商品ID" IS NULL
+                ORDER BY recent.rev DESC LIMIT 50
+            """)).fetchall()
+
+            # 新品總業績 + 整體業績佔比
+            new_rev_total = sum(float(r[3] or 0) for r in new_rows)
+            total_row = c.execute(text(f"""
+                SELECT COALESCE(SUM(CAST("總業績" AS FLOAT)), 0)
+                FROM realtime_sales_monthly
+                WHERE CAST("日期" AS DATE) >= CURRENT_DATE - INTERVAL '{days_recent} days'
+            """)).fetchone()
+            total_rev = float(total_row[0] or 0)
+
+            # 子品類分佈
+            sub_dist = c.execute(text(f"""
+                WITH recent AS (
+                    SELECT "商品ID", "商品分類L1",
+                           SUM(CAST("總業績" AS FLOAT)) AS rev
+                    FROM realtime_sales_monthly
+                    WHERE CAST("日期" AS DATE) >= CURRENT_DATE - INTERVAL '{days_recent} days'
+                    GROUP BY "商品ID", "商品分類L1"
+                ),
+                early AS (
+                    SELECT DISTINCT "商品ID"
+                    FROM realtime_sales_monthly
+                    WHERE CAST("日期" AS DATE) BETWEEN
+                          CURRENT_DATE - INTERVAL '{days_recent + days_baseline} days' AND
+                          CURRENT_DATE - INTERVAL '{days_recent + 1} days'
+                )
+                SELECT COALESCE(recent."商品分類L1", '其他') AS cat,
+                       COUNT(*) AS sku_count,
+                       SUM(recent.rev) AS rev
+                FROM recent
+                LEFT JOIN early ON recent."商品ID" = early."商品ID"
+                WHERE early."商品ID" IS NULL
+                GROUP BY recent."商品分類L1"
+                ORDER BY 3 DESC LIMIT 10
+            """)).fetchall()
+
+            # 新品整體日業績曲線
+            new_ids = [r[0] for r in new_rows]
+            if new_ids:
+                # 用 ANY array 比較
+                daily_rows = c.execute(text(f"""
+                    SELECT "日期", SUM(CAST("總業績" AS FLOAT)) AS rev
+                    FROM realtime_sales_monthly
+                    WHERE "商品ID" = ANY(:ids)
+                      AND CAST("日期" AS DATE) >= CURRENT_DATE - INTERVAL '{days_recent} days'
+                    GROUP BY "日期" ORDER BY "日期" ASC
+                """), {'ids': new_ids}).fetchall()
+            else:
+                daily_rows = []
+
+        return {
+            'found': len(new_rows) > 0,
+            'period': f"近 {days_recent} 天（vs 前 {days_baseline} 天 baseline）",
+            'kpis': {
+                'new_count': len(new_rows),
+                'new_revenue': new_rev_total,
+                'total_revenue': total_rev,
+                'new_pct': new_rev_total / total_rev * 100 if total_rev else 0,
+                'top1_revenue': float(new_rows[0][3]) if new_rows else 0,
+                'days_recent': days_recent,
+            },
+            'new_products': [
+                {'id': r[0], 'name': r[1], 'category': r[2] or '—',
+                 'revenue': float(r[3] or 0), 'qty': int(r[4] or 0),
+                 'orders': int(r[5] or 0), 'first_seen': str(r[6])}
+                for r in new_rows
+            ],
+            'sub_categories': [
+                {'name': r[0], 'sku_count': int(r[1]),
+                 'revenue': float(r[2] or 0)} for r in sub_dist
+            ],
+            'daily_total': [
+                {'date': str(r[0]), 'revenue': float(r[1] or 0)}
+                for r in daily_rows
+            ],
+        }
+    except Exception as e:
+        sys_log.error(f"[query_new_products] {e}")
+        return {'found': False, 'error': str(e)}
+
+
 def query_forecast_pre_event(event_name: str, event_date: str,
                              before_days: int = 14, after_days: int = 7) -> dict:
    """檔期前瞻：給定檔期日 + 名稱，回傳：
--- a/services/openclaw_bot/menu_keyboards.py
+++ b/services/openclaw_bot/menu_keyboards.py
@@ -220,6 +220,7 @@ def _submenu_reports():
             ('👥 客戶/訂單分析', 'cmd:ppt:customer')),
        _row(('🎯 檔期前瞻報告', 'await:forecast_event'),
             ('🆚 多活動比較', 'await:promo_compare')),
+        _row(('🆕 新品 30 天追蹤', 'cmd:ppt:new_product'),),
    ])


--- a/services/ppt_generator.py
+++ b/services/ppt_generator.py
@@ -61,6 +61,7 @@ TEMPLATE_VERSIONS = {
    'customer':   'v3.1.0', # 2026-05-03 客戶/訂單分析（簡化 RFM，受資料層 user_id 限制）
    'forecast_pre_event': 'v3.1.0', # 2026-05-03 檔期前瞻報（baseline × lift_factor 預測 + 去年同檔期）
    'promo_compare': 'v3.1.0', # 2026-05-03 多活動 ROI 並排比較
+    'new_product':   'v3.1.0', # 2026-05-03 新品 30 天追蹤（PM/採購）
 }


@@ -2951,6 +2952,229 @@ def generate_vendor_ppt(yr, mo, db_data, ai_text: str) -> str:
    return path


+# ── 新品 30 天追蹤報告 ──────────────────────────────────────────────────
+def generate_new_product_ppt(db_data: dict, ai_text: str) -> str:
+    """新品 30 天追蹤報告 v3.1（PM/採購用）
+    P1 封面：含新品數徽章 + 業績佔比
+    P2 KPI 摘要 + 業績佔比評估
+    P3 新品整體日業績曲線（爬榜軌跡）
+    P4 新品依品類分佈（橫條 + 業績/SKU 數雙軸概念）
+    P5-P7 新品 TOP 50 列表（自動分頁，含品類）
+    P8 AI PM 戰術洞察（明星新品 / 該扶植 / 該下架）
+    P9 附錄
+    """
+    from pptx import Presentation
+    from pptx.util import Cm
+
+    prs = Presentation()
+    prs.slide_width  = Cm(33.87)
+    prs.slide_height = Cm(19.05)
+    W = 33.87
+
+    period = db_data.get('period', '')
+    kpis = db_data.get('kpis', {}) or {}
+    new_prods = db_data.get('new_products', []) or []
+    sub_cats = db_data.get('sub_categories', []) or []
+    daily_total = db_data.get('daily_total', []) or []
+
+    new_count = int(kpis.get('new_count', 0))
+    new_rev = float(kpis.get('new_revenue', 0))
+    total_rev = float(kpis.get('total_revenue', 0))
+    new_pct = float(kpis.get('new_pct', 0))
+
+    # 新品強度徽章
+    if new_pct >= 8:
+        strength_label, strength_color = '新品力強勁', '2A7A3F'
+    elif new_pct >= 3:
+        strength_label, strength_color = '新品力穩健', 'B88416'
+    elif new_pct >= 1:
+        strength_label, strength_color = '新品力偏弱', 'C96442'
+    else:
+        strength_label, strength_color = '新品力疲弱', 'B5342F'
+
+    # ── P1 封面 ──────────────────────────────────────────────
+    slide = prs.slides.add_slide(prs.slide_layouts[6])
+    H = 19.05
+    _add_rect(slide, 0, 0, W, H, _BG_PAPER)
+    _add_rect(slide, 0, 0, 3.0, H, "2A7A3F")
+    _add_rect(slide, 2.85, 0, 0.15, H, _BRAND_OG2)
+    _add_rect(slide, W - 6.0, 0, 6.0, 0.45, _BRAND_OG2)
+    _add_rect(slide, W - 6.0, 0.45, 6.0, 0.12, "2A7A3F")
+    _add_rect(slide, 4.0, 8.4, 22.0, 0.06, "2A7A3F")
+
+    _add_rect(slide, 3.8, 1.4, 4.8, 0.85, _BRAND_OG2)
+    _add_text(slide, "OPENCLAW", 3.8, 1.42, 4.8, 0.81,
+              bold=True, size=12, color=_WHITE, align="center", valign="middle",
+              latin_font=_FONT_LABEL)
+    _add_text(slide, "NEW PRODUCT  ·  30-DAY TRACKING  ·  AI INSIGHT",
+              3.8, 2.45, 22, 0.55,
+              bold=True, size=10, color=_BRAND_OG2,
+              latin_font=_FONT_LABEL)
+    _add_text(slide, f"新品追蹤報告\n{period}",
+              3.8, 3.2, 25, 5.0,
+              bold=True, size=42, color=_DARK_TEXT,
+              latin_font=_FONT_DISPLAY, ea_font=_FONT_BODY_EA)
+    _add_rect(slide, W - 9.0, 3.4, 5.0, 1.1, strength_color)
+    _add_text(slide, f"新品力：{strength_label}",
+              W - 9.0, 3.45, 5.0, 1.0,
+              bold=True, size=14, color=_WHITE, align="center", valign="middle",
+              ea_font=_FONT_BODY_EA)
+    _add_text(slide,
+              f"🆕 {new_count} 款新品   ·   業績 NT${new_rev/10000:.1f}萬"
+              f"（佔總業績 {new_pct:.1f}%）",
+              3.8, 8.7, 27, 0.85,
+              bold=True, size=14, color=_BRAND_OG2,
+              latin_font=_FONT_DISPLAY, ea_font=_FONT_BODY_EA)
+
+    # 三個亮點
+    if new_prods:
+        top1 = new_prods[0]
+        pitch_y = 10.2
+        _add_rect(slide, 3.8, pitch_y, 0.45, 1.5, "2A7A3F")
+        _add_text(slide, "🏆  最強新品",
+                  4.4, pitch_y + 0.1, 27, 0.55,
+                  bold=True, size=11, color="2A7A3F",
+                  ea_font=_FONT_BODY_EA, latin_font=_FONT_LABEL)
+        _add_text(slide,
+                  f"{top1.get('name','')[:40]}  "
+                  f"NT${float(top1.get('revenue',0)):,.0f}（{top1.get('category','—')}）",
+                  4.4, pitch_y + 0.7, 27, 0.75,
+                  size=12, color=_DARK_TEXT,
+                  latin_font=_FONT_DISPLAY, ea_font=_FONT_BODY_EA)
+
+    if sub_cats:
+        top_cat = sub_cats[0]
+        pitch_y2 = 12.1
+        _add_rect(slide, 3.8, pitch_y2, 0.45, 1.5, "B88416")
+        _add_text(slide, "📊  新品集中品類",
+                  4.4, pitch_y2 + 0.1, 27, 0.55,
+                  bold=True, size=11, color="B88416",
+                  ea_font=_FONT_BODY_EA, latin_font=_FONT_LABEL)
+        _add_text(slide,
+                  f"{top_cat.get('name','')[:30]}  "
+                  f"{top_cat.get('sku_count', 0)} 款新品  "
+                  f"業績 NT${top_cat.get('revenue', 0)/10000:.1f}萬",
+                  4.4, pitch_y2 + 0.7, 27, 0.75,
+                  size=12, color=_DARK_TEXT,
+                  latin_font=_FONT_DISPLAY, ea_font=_FONT_BODY_EA)
+
+    pitch_y3 = 14.0
+    _add_rect(slide, 3.8, pitch_y3, 0.45, 1.5, "C96442")
+    _add_text(slide, "🎯  業績佔比評估",
+              4.4, pitch_y3 + 0.1, 27, 0.55,
+              bold=True, size=11, color="C96442",
+              ea_font=_FONT_BODY_EA, latin_font=_FONT_LABEL)
+    _add_text(slide,
+              f"新品 {new_pct:.1f}% — " + (
+                  "業界一流（>8%）" if new_pct >= 8 else
+                  "健康（3-8%）" if new_pct >= 3 else
+                  "需強化（<3%）"
+              ) + "；建議目標 5-10%（電商業界平均）",
+              4.4, pitch_y3 + 0.7, 27, 0.75,
+              size=12, color=_DARK_TEXT,
+              latin_font=_FONT_DISPLAY, ea_font=_FONT_BODY_EA)
+
+    _add_text(slide, "Generated by OpenClaw AI Agent",
+              W - 7.5, H - 1.4, 7.0, 0.5,
+              size=9, color=_SUBTEXT, align="right", latin_font=_FONT_LABEL)
+    _add_text(slide, f"📅  {datetime.now().strftime('%Y/%m/%d %H:%M')}",
+              W - 7.5, H - 1.95, 7.0, 0.5,
+              bold=True, size=11, color=_BRAND_OG2, align="right",
+              latin_font=_FONT_DISPLAY, ea_font=_FONT_BODY_EA)
+    _add_footer(slide, W)
+
+    # ── P2 KPI ────────────────────────────────────────────
+    s2 = prs.slides.add_slide(prs.slide_layouts[6])
+    _add_rect(s2, 0, 0, W, _SLIDE_H, _BG_PAPER)
+    _add_header(s2, f"新品追蹤 KPI — {period}")
+    avg_rev = new_rev / new_count if new_count else 0
+    cards = [
+        (_KPI_CARAMEL, "新品總數", f"{new_count} 款", None, "近 30 天進榜"),
+        (_KPI_HONEY, "新品業績", f"NT${new_rev/10000:.1f}萬", None, "30 天累積"),
+        (_KPI_MAHOGANY, "業績佔比", f"{new_pct:.1f}%", None, "vs 整體業績"),
+        (_KPI_EARTH, "新品平均", f"NT${avg_rev/10000:.1f}萬", None, "單品 30 天均值"),
+    ]
+    for i, (col, lbl, val, dp, dl) in enumerate(cards):
+        _kpi_card_v2(s2, i * 7.8 + 0.5, 1.95, 7.4, 4.5,
+                     col, lbl, val, delta_pct=dp, delta_label=dl, sub=dl)
+
+    summary_text = (ai_text or '')[:400] if ai_text else "(暫無 AI 分析)"
+    _add_rect(s2, 0.5, 7.0, W - 1.0, 0.7, "2A7A3F")
+    _add_text(s2, "🆕  新品力解讀",
+              1.1, 7.05, W - 1.5, 0.6, bold=True, size=13, color=_WHITE,
+              valign="middle", ea_font=_FONT_BODY_EA)
+    _add_rect(s2, 0.5, 7.7, W - 1.0, 6.4, _WHITE, line_hex=_SUBTLE)
+    _add_rect(s2, 0.5, 7.7, 0.4, 6.4, "2A7A3F")
+    _add_text(s2, summary_text,
+              1.2, 7.95, W - 2.0, 5.9,
+              size=13, color=_DARK_TEXT, wrap=True,
+              latin_font=_FONT_DISPLAY, ea_font=_FONT_BODY_EA)
+    _add_footer(s2, W)
+
+    # ── P3 新品整體日業績曲線 ───────────────────────────────
+    if daily_total:
+        s3 = prs.slides.add_slide(prs.slide_layouts[6])
+        _add_rect(s3, 0, 0, W, _SLIDE_H, _BG_PAPER)
+        _add_header(s3, "新品整體日業績走勢（爬榜軌跡）")
+        d_dates = [d.get('date', '') for d in daily_total]
+        d_revs = [float(d.get('revenue', 0)) for d in daily_total]
+        chart_w = W - 0.8
+        chart_h = 12.5
+        buf = _mpl_line_chart_png(
+            d_dates, d_revs, prev_vals=None,
+            total_width_cm=chart_w, total_height_cm=chart_h,
+            title="新品 30 天日業績走勢",
+            curr_label="新品合計"
+        )
+        if buf:
+            _add_image_from_buf(s3, buf, 0.4, 1.95, chart_w, chart_h)
+        _add_footer(s3, W)
+
+    # ── P4 新品依品類分佈 ────────────────────────────────
+    if sub_cats:
+        s4 = prs.slides.add_slide(prs.slide_layouts[6])
+        _add_rect(s4, 0, 0, W, _SLIDE_H, _BG_PAPER)
+        _add_header(s4, "新品依品類分佈")
+        names = [c.get('name', '')[:14] for c in sub_cats[:8]]
+        revs = [float(c.get('revenue', 0)) for c in sub_cats[:8]]
+        chart_w = W - 0.8
+        chart_h = 11.5
+        buf = _mpl_horiz_bar_png(names, revs,
+                                 total_width_cm=chart_w,
+                                 total_height_cm=chart_h,
+                                 value_unit="萬",
+                                 title="新品業績排行（依品類）",
+                                 highlight_top_n=3)
+        if buf:
+            _add_image_from_buf(s4, buf, 0.4, 1.95, chart_w, chart_h)
+
+        # 底部結論
+        _add_rect(s4, 0.4, 14.0, W - 0.8, 1.4, _BRAND_OG2)
+        cat_summary = '   ·   '.join(
+            f"{c.get('name','')[:8]} {c.get('sku_count',0)} 款"
+            for c in sub_cats[:5]
+        )
+        _add_text(s4, f"📊  品類新品數：{cat_summary}",
+                  0.7, 14.15, W - 1.4, 1.1,
+                  bold=True, size=12, color=_WHITE, valign="middle", wrap=True,
+                  latin_font=_FONT_DISPLAY, ea_font=_FONT_BODY_EA)
+        _add_footer(s4, W)
+
+    # ── P5-P7 新品 TOP 50 ──────────────────────────────────
+    _product_table_slide(prs, f"新品 TOP {min(50, len(new_prods))} — {period}",
+                         new_prods, max_items=50)
+
+    # ── P8 AI 洞察 ─────────────────────────────────────────
+    _ai_insight_slide(prs, ai_text)
+
+    # ── P9 附錄 ────────────────────────────────────────────
+    _appendix_slide(prs, 'new_product', period)
+
+    path = _new_path("new_product")
+    prs.save(path)
+    return path
+
+
 # ── 多活動 ROI 橫向比較報告 ─────────────────────────────────────────────
 def generate_promo_compare_ppt(label: str, db_data: dict, ai_text: str) -> str:
    """多活動 ROI 比較報告：2-N 個促銷活動並排比較